Artificial Intelligence has become a key enabler in today’s Oil & Gas industry, particularly under market conditions characterized by volatile oil prices and increasing pressure to reduce operating costs and energy consumption. AI-based solutions support both onshore and offshore operations by optimizing energy efficiency, improving throughput in surface processing equipment and wells, and enhancing production quality through better control of parameters such as Basic Sediment and Water (BSW).
In this context, the development of predictive models for inferential sensors is a strategic approach to improve real-time monitoring and decision-making. These models enable the estimation of critical production and reservoir variables—including oil volume, total liquid rate, gas volume, water cut, dynamic fluid level, and reservoir pressure—without relying exclusively on physical sensors, thereby reducing CAPEX, OPEX, and operational risk while maintaining production reliability.